Techniques for studying the human microcirculation in vivo have rapidly evolved in the last few decades. Capillaroscopy is a technique that uses an intravital microscope for imaging the microvascular bed. It has long been the only technique available to study the microcirculation of easy-accessible tissue, such as the skin or the capillary bed under the nail. The microscope-based configuration, however, makes capillaroscopy systems inaccessible to deep-lying tissue. More-recent studies describe the use of Orthogonal Polarization Spectral (OPS) imaging [3,4,18,20,23,25,30] or Side-stream Dark Field (SDF) imaging [13]. These techniques are basically hand-held microscopes with an advanced way of target illumination resulting in a higher contrast than is obtained with capillaroscopy systems [23]. Another big advance of these systems is the long probe, which makes the tip accessible to organ surfaces that cannot be reached with intravital microscopy. OPS-imaging systems have proved to be valuable in studying the microcirculation of, e.g., the human nail fold [23], sublingual tissue [20] during sepsis [30], brain tissue during aneurysm surgery [25], the colon in mice [3], skin flaps in mice [18] and in hamsters [10]. OPS studies are mainly observatory. Some OPS researchers used a semi-quantitative way of analysis where blood flow is scored [4,30] (no flow, intermittent flow, sluggish flow, continuous flow) in three vessel types: small (10-25 μm), medium (25-50 μm) and large (50-100 μm).
Besides quantitative geometry parameters for describing the microcirculation, there is increasing interest for quantification of microcirculatory blood velocity. Several methods have been described to determine velocity from a sequence of video frames [12,14,19,20]. Spatial correlation techniques [20] select a patch from a reference frame and trace that patch in subsequent frames. In the microcirculation, such patches undergo morphological changes. This is caused by the fact that cells near the vessel wall travel at a lower speed than cells at the center, and the fact that radial motion and motion perpendicular to the focal plane is also observed, e.g., due to vessel curvature. Optical flow techniques [12] rely on the fact that the intensity of traceable objects remains constant over time. Cells or clusters of cells also move in a direction perpendicular to the focal plane. This causes the intensity of objects to change in time as other cells overlap, which does not meet the continuous object intensity constraint. The use of anisotropic diffusion filters may overcome this problem yet require the existence of relatively large plasma gaps in order to detect large plugs of red blood cells.
The use of space-time diagrams for velocity estimation [14] has been used in many studies of the microcirculation [7,15,16,17,19,23,32]. These diagrams plot the longitudinal intensity profile of a straight vessel segment versus time. The diagonal bands in a space-time diagram represent objects traveling through the vessel. Bright bands are observed when a plasma gap or a white blood cell passes the vessel, while dark bands represent the presence of red blood cells. A big advantage of this technique is the fact that it includes all available space and time data for velocity estimation and the fact that the investigator receives immediate optical feedback of flow type from the lines that appear in the space-time diagram.
Klyscz and coworkers [17] described a computer program that features techniques for quantitative analysis of microvasculatory videos. Local vessel width is determined with an on-screen caliper; vessel length is obtained using a drawing tool that allows interactive tracing of vessels; it provides the functional capillary density (FCD), which is defined as the ratio of the total vessel length (L) (traced by the user) and the image area (A) of interest. It also estimates blood velocity using space-time diagrams [14,17,19]. Their method requires the user to draw a straight line at a vessel's centerline. The line indicates the location for acquiring image data to generate the space-time diagram. Although the program is unique in its field, it requires a large extent of user interaction, which increases observer bias and makes analysis time consuming.